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Khan R, Rahman NU, Hayat MF, Ghernaout D, Salih AAM, Ashraf GA, Samad A, Mahmood MA, Rahman N, Sohail M, Iqbal S, Abdullaev S, Khan A. Unveiling cutting-edge developments: architectures and nanostructured materials for application in optoelectronic artificial synapses. NANOSCALE 2024. [PMID: 39011743 DOI: 10.1039/d4nr00904e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
One possible result of low-level characteristics in the traditional von Neumann formulation system is brain-inspired photonics technology based on human brain idea. Optoelectronic neural devices, which are accustomed to imitating the sensory role of biological synapses by adjusting connection measures, can be used to fabricate highly reliable neurologically calculating devices. In this case, nanosized materials and device designs are attracting attention since they provide numerous potential benefits in terms of limited cool contact, rapid transfer fluidity, and the capture of photocarriers. In addition, the combination of classic nanosized photodetectors with recently generated digital synapses offers promising results in a variety of practical applications, such as data processing and computation. Herein, we present the progress in constructing improved optoelectronic synaptic devices that rely on nanomaterials, for example, 0-dimensional (quantum dots), 1-dimensional, and 2-dimensional composites, besides the continuously developing mixed heterostructures. Furthermore, the challenges and potential prospects linked with this field of study are discussed in this paper.
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Affiliation(s)
- Rajwali Khan
- National Water and Energy Center, United Arab Emirates University, Al Ain, 15551, United Arab Emirates.
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | - Naveed Ur Rahman
- National Water and Energy Center, United Arab Emirates University, Al Ain, 15551, United Arab Emirates.
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | | | - Djamel Ghernaout
- Chemical Engineering Department, College of Engineering, University of Ha'il, PO Box 2440, Ha'il 81441, Saudi Arabia
- Chemical Engineering Department, Faculty of Engineering, University of Blida, PO Box 270, Blida 09000, Algeria
| | - Alsamani A M Salih
- Chemical Engineering Department, College of Engineering, University of Ha'il, PO Box 2440, Ha'il 81441, Saudi Arabia
- Department of Chemical Engineering, Faculty of Engineering, Al Neelain University, Khartoum 12702, Sudan
| | | | - Abdus Samad
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | | | - Nasir Rahman
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | - Mohammad Sohail
- Department of Physics, University of Lakki Marwat, Lakki Marwat, 2842, KP, Pakistan
| | - Shahid Iqbal
- Department of Physics, University of Wisconsin, La Crosse, WI 54601, USA
| | - Sherzod Abdullaev
- Senior Researcher, Engineering School, Central Asian University, Tashkent, Uzbekistan
- Senior Researcher, Scientific and Innovation Department, Tashkent State Pedagogical University, Uzbekistan
| | - Alamzeb Khan
- Yale University School of Medicine, New Haven, Connecticut, USA
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Guo Y, Lv M, Wang C, Ma J. Energy controls wave propagation in a neural network with spatial stimuli. Neural Netw 2024; 171:1-13. [PMID: 38091753 DOI: 10.1016/j.neunet.2023.11.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/16/2023] [Accepted: 11/19/2023] [Indexed: 01/29/2024]
Abstract
Nervous system has distinct anisotropy and some intrinsic biophysical properties enable neurons present various firing modes in neural activities. In presence of realistic electromagnetic fields, non-uniform radiation activates these neurons with energy diversity. By using a feasible model, energy function is obtained to predict the growth of synaptic connections of these neurons. Distribution of average value of the Hamilton energy function vs. intensity of noisy disturbance can predict the occurrence of coherence resonance, which the neural activities show high regularity by applying noisy disturbance with moderate intensity. From physical viewpoint, the average energy value has similar role average power for the neuron. Non-uniform spatial disturbance is applied and energy is injected into the neural network, statistical synchronization factor is calculated to predict the network synchronization stability and wave propagation. The intensity for field coupling is adaptively controlled by energy diversity between adjacent neurons. Local energy balance will terminate further growth of the coupling intensity; otherwise, heterogeneity is formed in the network due to energy diversity. Furthermore, memristive channel current is introduced into the neuron model for perceiving the effect of electromagnetic induction and radiation, and a memristive neuron is obtained. The circuit implement of memristive circuit depends on the connection to a magnetic flux-controlled memristor into the mentioned neural circuit in an additive branch circuit. The connection and activation of this memristive neural network are controlled under external spatial electromagnetic radiation by capturing enough field energy. Continuous energy collection and exchange generate energy diversity and synaptic connection is created to regulate the synchronous firing patterns and energy balance.
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Affiliation(s)
- Yitong Guo
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China
| | - Mi Lv
- Faculty of Engineering, China University of Petroleum-Beijing at Karamay, Karamay, 834000, Xinjiang, PR China
| | - Chunni Wang
- Department of Physics, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
| | - Jun Ma
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China; Department of Physics, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China
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Wang J, Ilyas N, Ren Y, Ji Y, Li S, Li C, Liu F, Gu D, Ang KW. Technology and Integration Roadmap for Optoelectronic Memristor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2307393. [PMID: 37739413 DOI: 10.1002/adma.202307393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/10/2023] [Indexed: 09/24/2023]
Abstract
Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.
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Affiliation(s)
- Jinyong Wang
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Nasir Ilyas
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Yujing Ren
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, 117585, Singapore
| | - Yun Ji
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Sifan Li
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
| | - Changcun Li
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Fucai Liu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Deen Gu
- School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 611731, P. R. China
| | - Kah-Wee Ang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore
- Institute of Materials Research and Engineering, A*STAR, Singapore, 138634, Singapore
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Lee KJ, Kim JH, Jeon S, Shin CW, Kim HR, Park HG, Kim J. Polarization-Dependent Memory and Erasure in Quantum Dots/Graphene Synaptic Devices. NANO LETTERS 2024; 24:2421-2427. [PMID: 38319957 DOI: 10.1021/acs.nanolett.4c00124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
We demonstrate excitatory and inhibitory properties in a single heterostructure consisting of two quantum dots/graphene synaptic elements using linearly polarized monochromatic light. Perovskite quantum dots and PbS quantum dots were used to increase and decrease photocurrent weights, respectively. The polarization-dependent photocurrent was realized by adding a polarizer in the middle of the PbS quantum dots/graphene and perovskite quantum dots/graphene elements. When linearly polarized light passed through the polarizer, both the lower excitatory and upper inhibitory devices were activated, with the lower device with the stronger response dominating to increase the current weight. In contrast, the polarized light was blocked by the polarizer, and the above device was only operated, reducing the current weight. Furthermore, two orthogonal polarizations of light were used to perform the sequential processes of potentiation and habituation. By adjustment of the polarization angle of light, not only the direction of the current weight but also its level was altered.
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Affiliation(s)
- Ki-Jeong Lee
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
| | - Jin Hyung Kim
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
| | - Sooin Jeon
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
| | - Chi Won Shin
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul 08826, Republic of Korea
| | - Ha-Reem Kim
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul 08826, Republic of Korea
| | - Hong-Gyu Park
- Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul 08826, Republic of Korea
| | - Jungkil Kim
- Department of Physics, Jeju National University, Jeju 63243, Republic of Korea
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Xin W, Zhong W, Shi Y, Shi Y, Jing J, Xu T, Guo J, Liu W, Li Y, Liang Z, Xin X, Cheng J, Hu W, Xu H, Liu Y. Low-Dimensional-Materials-Based Photodetectors for Next-Generation Polarized Detection and Imaging. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2306772. [PMID: 37661841 DOI: 10.1002/adma.202306772] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/22/2023] [Indexed: 09/05/2023]
Abstract
The vector characteristics of light and the vectorial transformations during its transmission lay a foundation for polarized photodetection of objects, which broadens the applications of related detectors in complex environments. With the breakthrough of low-dimensional materials (LDMs) in optics and electronics over the past few years, the combination of these novel LDMs and traditional working modes is expected to bring new development opportunities in this field. Here, the state-of-the-art progress of LDMs, as polarization-sensitive components in polarized photodetection and even the imaging, is the main focus, with emphasis on the relationship between traditional working principle of polarized photodetectors (PPs) and photoresponse mechanisms of LDMs. Particularly, from the view of constitutive equations, the existing works are reorganized, reclassified, and reviewed. Perspectives on the opportunities and challenges are also discussed. It is hoped that this work can provide a more general overview in the use of LDMs in this field, sorting out the way of related devices for "more than Moore" or even the "beyond Moore" research.
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Affiliation(s)
- Wei Xin
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Weiheng Zhong
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Yujie Shi
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Yimeng Shi
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Jiawei Jing
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Tengfei Xu
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Jiaxiang Guo
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Weizhen Liu
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Yuanzheng Li
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Zhongzhu Liang
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Xing Xin
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Jinluo Cheng
- GPL Photonics Laboratory, State Key Laboratory of Luminescence and Applications, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, China
| | - Weida Hu
- State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China
| | - Haiyang Xu
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Yichun Liu
- Key Laboratory of UV-Emitting Materials and Technology, Ministry of Education, Northeast Normal University, Changchun, Jilin, 130024, China
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Wang Y, Yin L, Huang S, Xiao R, Zhang Y, Li D, Pi X, Yang D. Silicon-Nanomembrane-Based Broadband Synaptic Phototransistors for Neuromorphic Vision. NANO LETTERS 2023; 23:8460-8467. [PMID: 37721358 DOI: 10.1021/acs.nanolett.3c01853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Neuromorphic vision has been attracting much attention due to its advantages over conventional machine vision (e.g., lower data redundancy and lower power consumption). Here we develop synaptic phototransistors based on the silicon nanomembrane (Si NM), which are coupled with lead sulfide quantum dots (PbS QDs) and poly(3-hexylthiophene) (P3HT) to form a heterostructure with distinct photogating. Synaptic phototransistors with optical stimulation have outstanding synaptic functionalities ranging from ultraviolet (UV) to near-infrared (NIR). The broadband synaptic functionalities enable an array of synaptic phototransistors to achieve the perception of brightness and color. In addition, an array of synaptic phototransistors is capable of simultaneous sensing, processing, and memory, which well mimics human vision.
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Affiliation(s)
- Yue Wang
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
| | - Lei Yin
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Shijie Huang
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Rulei Xiao
- National Laboratory of Solid State Microstructures, Collaborative Innovation Center of Advanced Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, China
| | - Yiqiang Zhang
- School of Materials Science and Engineering & College of Chemistry, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Dongke Li
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
| | - Xiaodong Pi
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
| | - Deren Yang
- State Key Laboratory of Silicon and Advanced Semiconductor Materials & School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices, ZJU-Hangzhou Global Scientific and Technological Innovation Centre, Zhejiang University, Hangzhou, Zhejiang 311215, China
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7
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Vats G, Hodges B, Ferguson AJ, Wheeler LM, Blackburn JL. Optical Memory, Switching, and Neuromorphic Functionality in Metal Halide Perovskite Materials and Devices. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2205459. [PMID: 36120918 DOI: 10.1002/adma.202205459] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/20/2022] [Indexed: 06/15/2023]
Abstract
Metal halide perovskite based materials have emerged over the past few decades as remarkable solution-processable optoelectronic materials with many intriguing properties and potential applications. These emerging materials have recently been considered for their promise in low-energy memory and information processing applications. In particular, their large optical cross-sections, high photoconductance contrast, large carrier-diffusion lengths, and mixed electronic/ionic transport mechanisms are attractive for enabling memory elements and neuromorphic devices that are written and/or read in the optical domain. Here, recent progress toward memory and neuromorphic functionality in metal halide perovskite materials and devices where photons are used as a critical degree of freedom for switching, memory, and neuromorphic functionality is reviewed.
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Affiliation(s)
- Gaurav Vats
- National Renewable Energy Laboratory, Golden, CO, 80401, USA
- Department of Physics and Astronomy, Katholieke Universiteit Leuven, Celestijnenlaan 200D, Leuven, B-3001, Belgium
| | - Brett Hodges
- National Renewable Energy Laboratory, Golden, CO, 80401, USA
| | | | - Lance M Wheeler
- National Renewable Energy Laboratory, Golden, CO, 80401, USA
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Zhang Y, Huang Z, Jiang J. Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS 2023; 24:2186689. [PMID: 37007672 PMCID: PMC10054230 DOI: 10.1080/14686996.2023.2186689] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/16/2023] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.
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Affiliation(s)
- Yi Zhang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
| | - Zhuohui Huang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
| | - Jie Jiang
- Hunan Key Laboratory of Nanophotonics and Devices, Hunan Key Laboratory of Super Microstructure and Ultrafast Process, School of Physics and Electronics, Central South University, Changsha, Hunan, China
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Wang S, Chen H, Liu T, Wei Y, Yao G, Lin Q, Han X, Zhang C, Huang H. Retina-Inspired Organic Photonic Synapses for Selective Detection of SWIR Light. Angew Chem Int Ed Engl 2023; 62:e202213733. [PMID: 36418239 DOI: 10.1002/anie.202213733] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 11/06/2022] [Accepted: 11/23/2022] [Indexed: 11/25/2022]
Abstract
Photonic synapses with the dual function of optical signal detection and information processing can simulate human visual system. However, photonic synapses with selective detection of short-wavelength infrared (SWIR) light have never been reported, which can not only broaden the human vision region but also integrate neuromorphic computation and infrared optical communication. Here, organic photonic synapses based on a new donor-acceptor copolymer P1 are fabricated, which exhibit excellent synaptic characteristics with selective detection for SWIR and extremely low energy consumption (2.85 fJ). The working mechanism is rooted in energy level barriers and unbalanced charge transportation. Moreover, these photonic synapses demonstrate excellent performance in multi-signal logic editing, letter imaging and memory with noise reduction function. This contribution provides ideas of constructing selective-response synapses for artificial visual system and neuromorphic computing.
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Affiliation(s)
- Song Wang
- College of Materials Science and Opto-Electronic Technology, Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, and CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Hao Chen
- College of Materials Science and Opto-Electronic Technology, Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, and CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Tianhua Liu
- College of Materials Science and Opto-Electronic Technology, Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, and CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Yanan Wei
- College of Materials Science and Opto-Electronic Technology, Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, and CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Guo Yao
- National Laboratory of Solid State Microstructures, School of Physics, and Collaborative Innovation Center for Advanced Microstructures, Nanjing University, Nanjing, 210093, P.R. China
| | - Qijie Lin
- College of Materials Science and Opto-Electronic Technology, Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, and CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Xiao Han
- College of Materials Science and Opto-Electronic Technology, Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, and CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
| | - Chunfeng Zhang
- National Laboratory of Solid State Microstructures, School of Physics, and Collaborative Innovation Center for Advanced Microstructures, Nanjing University, Nanjing, 210093, P.R. China
| | - Hui Huang
- College of Materials Science and Opto-Electronic Technology, Center of Materials Science and Optoelectronics Engineering, CAS Center for Excellence in Topological Quantum Computation, and CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing, 100049, P.R. China
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10
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Zhang M, Chi Z, Wang G, Fan Z, Wu H, Yang P, Yang J, Yan P, Sun Z. An Irradiance-Adaptable Near-Infrared Vertical Heterojunction Phototransistor. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2205679. [PMID: 35986669 DOI: 10.1002/adma.202205679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Bioinspired artificial visual perception devices with the optical environment-adaptable function have attracted significant attention for their promising potential in applications like robotics and machine vision. In this regard, a photodetector with in-sensor adaptability is longed for in terms of complexity, efficiency, and cost. Here, a near-infrared phototransistor with a benign light irradiance-adaptability is presented. The phototransistor uses a vertically stacking graphene/lead sulfide quantum dots/graphene heterojunction as the conductive channel. Compared with ordinary lead sulfide quantum dots-decorated graphene phototransistors, the present device demonstrates a faster photoresponse speed and an abnormal transfer characteristic. The latter characteristic is induced by the gate voltage-tunable Fermi level in the heterojunction and the abundant electron trap states in the quantum dot film, which jointly results in an intense dependence of the photoresponse on the gate voltage. The dynamic trapping and de-trapping processes in the quantum dot film enable the inhibition or potentiation of the photoresponse, based on which the photopic or scotopic adaptation behavior of the human retina is successfully mimicked, respectively. By providing an irradiance-adaptable photodetector with a spectral response beyond visible light, this work should inspire future research on artificial environment-adaptable perception devices.
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Affiliation(s)
- Mengyu Zhang
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Zhiguo Chi
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Guoqing Wang
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Zelong Fan
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Honglei Wu
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Ping Yang
- The Key laboratory of Adaptive Optics, Chinese Academy of Sciences, Chengdu, Sichuan, 610209, China
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, Sichuan, 610209, China
| | - Junbo Yang
- College of Arts & Science, National University of Defense Technology, Changsha, 410003, China
| | - Peiguang Yan
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Zhenhua Sun
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China
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11
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Kim S, Roh H, Im M. Artificial Visual Information Produced by Retinal Prostheses. Front Cell Neurosci 2022; 16:911754. [PMID: 35734216 PMCID: PMC9208577 DOI: 10.3389/fncel.2022.911754] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/18/2022] [Indexed: 11/18/2022] Open
Abstract
Numerous retinal prosthetic systems have demonstrated somewhat useful vision can be restored to individuals who had lost their sight due to outer retinal degenerative diseases. Earlier prosthetic studies have mostly focused on the confinement of electrical stimulation for improved spatial resolution and/or the biased stimulation of specific retinal ganglion cell (RGC) types for selective activation of retinal ON/OFF pathway for enhanced visual percepts. To better replicate normal vision, it would be also crucial to consider information transmission by spiking activities arising in the RGC population since an incredible amount of visual information is transferred from the eye to the brain. In previous studies, however, it has not been well explored how much artificial visual information is created in response to electrical stimuli delivered by microelectrodes. In the present work, we discuss the importance of the neural information for high-quality artificial vision. First, we summarize the previous literatures which have computed information transmission rates from spiking activities of RGCs in response to visual stimuli. Second, we exemplify a couple of studies which computed the neural information from electrically evoked responses. Third, we briefly introduce how information rates can be computed in the representative two ways – direct method and reconstruction method. Fourth, we introduce in silico approaches modeling artificial retinal neural networks to explore the relationship between amount of information and the spiking patterns. Lastly, we conclude our review with clinical implications to emphasize the necessity of considering visual information transmission for further improvement of retinal prosthetics.
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Affiliation(s)
- Sein Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, South Korea
| | - Hyeonhee Roh
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, South Korea
- School of Electrical Engineering, College of Engineering, Korea University, Seoul, South Korea
| | - Maesoon Im
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, South Korea
- Division of Bio-Medical Science & Technology, KIST School, University of Science and Technology, Seoul, South Korea
- *Correspondence: Maesoon Im, ,
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12
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Pei Y, Yan L, Wu Z, Lu J, Zhao J, Chen J, Liu Q, Yan X. Artificial Visual Perception Nervous System Based on Low-Dimensional Material Photoelectric Memristors. ACS NANO 2021; 15:17319-17326. [PMID: 34541840 DOI: 10.1021/acsnano.1c04676] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The visual perception system is the most important system for human learning since it receives over 80% of the learning information from the outside world. With the exponential growth of artificial intelligence technology, there is a pressing need for high-energy and area-efficiency visual perception systems capable of processing efficiently the received natural information. Currently, memristors with their elaborate dynamics, excellent scalability, and information (e.g., visual, pressure, sound, etc.) perception ability exhibit tremendous potential for the application of visual perception. Here, we propose a fully memristor-based artificial visual perception nervous system (AVPNS) which consists of a quantum-dot-based photoelectric memristor and a nanosheet-based threshold-switching (TS) memristor. We use a photoelectric and a TS memristor to implement the synapse and leaky integrate-and-fire (LIF) neuron functions, respectively. With the proposed AVPNS we successfully demonstrate the biological image perception, integration and fire, as well as the biosensitization process. Furthermore, the self-regulation process of a speed meeting control system in driverless automobiles can be accurately and conceptually emulated by this system. Our work shows that the functions of the biological visual nervous system may be systematically emulated by a memristor-based hardware system, thus expanding the spectrum of memristor applications in artificial intelligence.
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Affiliation(s)
- Yifei Pei
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Lei Yan
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Zuheng Wu
- School of Integrated Circuits, Anhui University, Hefei, Anhui 230601, P. R. China
| | - Jikai Lu
- Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics of Chinese Academy of Sciences, Beijing, 100029, P. R. China
| | - Jianhui Zhao
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China
| | - Jingsheng Chen
- Department of Materials Science and Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Qi Liu
- Frontier Institute of Chip and System Fudan University Shanghai 200433, P. R. China
| | - Xiaobing Yan
- National-Local Joint Engineering Laboratory of New Energy Photovoltaic Devices, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electron and Information Engineering, Hebei University, Baoding 071002, P. R. China
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13
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Shen C, Gao X, Chen C, Ren S, Xu JL, Xia YD, Wang SD. ZnO nanowire optoelectronic synapse for neuromorphic computing. NANOTECHNOLOGY 2021; 33:065205. [PMID: 34736234 DOI: 10.1088/1361-6528/ac3687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/04/2021] [Indexed: 06/13/2023]
Abstract
Artificial synapses that integrate functions of sensing, memory and computing are highly desired for developing brain-inspired neuromorphic hardware. In this work, an optoelectronic synapse based on the ZnO nanowire (NW) transistor is achieved, which can be used to emulate both the short-term and long-term synaptic plasticity. Synaptic potentiation is present when the device is stimulated by light pulses, arising from the light-induced O2desorption and the persistent photoconductivity behavior of the ZnO NW. On the other hand, synaptic depression occurs when the device is stimulated by electrical pulses in dark, which is realized by introducing a charge trapping layer in the gate dielectric to trap carriers. Simulation of a neural network utilizing the ZnO NW synapses is carried out, demonstrating a high recognition accuracy over 90% after only 20 training epochs for recognizing the Modified National Institute of Standards and Technology digits. The present nanoscale optoelectronic synapse has great potential in the development of neuromorphic visual systems.
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Affiliation(s)
- Cong Shen
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, People's Republic of China
| | - Xu Gao
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, People's Republic of China
| | - Cheng Chen
- School of Optoelectronic Science and Engineering, Key Laboratory of Advanced Optical Manufacturing Technologies of Jiangsu Province, Soochow University, Suzhou, Jiangsu 215006, People's Republic of China
| | - Shan Ren
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, People's Republic of China
| | - Jian-Long Xu
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, People's Republic of China
| | - Yi-Dong Xia
- Department of Materials Science and Engineering, College of Engineering and Applied Sciences, Nanjing University, Nanjing, Jiangsu 210093, People's Republic of China
| | - Sui-Dong Wang
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, People's Republic of China
- Macao Institute of Materials Science and Engineering (MIMSE), Macau University of Science and Technology, Taipa 999078, Macau, People's Republic of China
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14
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Sun F, Lu Q, Feng S, Zhang T. Flexible Artificial Sensory Systems Based on Neuromorphic Devices. ACS NANO 2021; 15:3875-3899. [PMID: 33507725 DOI: 10.1021/acsnano.0c10049] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Emerging flexible artificial sensory systems using neuromorphic electronics have been considered as a promising solution for processing massive data with low power consumption. The construction of artificial sensory systems with synaptic devices and sensing elements to mimic complicated sensing and processing in biological systems is a prerequisite for the realization. To realize high-efficiency neuromorphic sensory systems, the development of artificial flexible synapses with low power consumption and high-density integration is essential. Furthermore, the realization of efficient coupling between the sensing element and the synaptic device is crucial. This Review presents recent progress in the area of neuromorphic electronics for flexible artificial sensory systems. We focus on both the recent advances of artificial synapses, including device structures, mechanisms, and functions, and the design of intelligent, flexible perception systems based on synaptic devices. Additionally, key challenges and opportunities related to flexible artificial perception systems are examined, and potential solutions and suggestions are provided.
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Affiliation(s)
- Fuqin Sun
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
- School of Nano Technology and Nano Bionics, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, P. R. China
| | - Qifeng Lu
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
| | - Simin Feng
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
| | - Ting Zhang
- i -Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, Suzhou 215123, P. R. China
- School of Nano Technology and Nano Bionics, University of Science and Technology of China, 96 Jinzhai Road, Hefei, Anhui 230026, P. R. China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
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15
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Wang X, Lu Y, Zhang J, Zhang S, Chen T, Ou Q, Huang J. Highly Sensitive Artificial Visual Array Using Transistors Based on Porphyrins and Semiconductors. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2021; 17:e2005491. [PMID: 33325607 DOI: 10.1002/smll.202005491] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/20/2020] [Indexed: 06/12/2023]
Abstract
Artificial visual systems with image sensing and storage functions have considerable potential in the field of artificial intelligence. Light-stimulated synaptic devices can be applied for neuromorphic computing to build artificial visual systems. Here, optoelectronic synaptic transistors based on 5,15-(2-hydroxyphenyl)-10,20-(4-nitrophenyl)porphyrin (TPP) and dinaphtho[2,3-b:2',3'-f ]thieno[3,2-b]thiophene (DNTT) are demonstrated. By utilizing stable TPP with high light absorption, the number of photogenerated carriers in the transport layer can be increased significantly. The devices exhibit high photosensitivity and tunable synaptic plasticity. The synaptic weight can be effectively modulated by the intensity, width, and wavelength of the light signals. Due to the high light absorption of TPP, an ultrasensitive artificial visual array based on these devices is developed, which can detect weak light signals as low as 1 µW cm-2 . Low-voltage operation is further demonstrated. Even with applied voltages as low as -70 µV, the devices can still show obvious responses, leading to an ultralow energy consumption of 1.4 fJ. The devices successfully demonstrate image sensing and storage functions, which can accurately identify visual information. In addition, the devices can preprocess information and achieve noise reduction. The excellent synaptic behavior of the TPP-based electronics suggests their good potential in the development of new intelligent visual systems.
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Affiliation(s)
- Xin Wang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Yang Lu
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Junyao Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Shiqi Zhang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Tianqi Chen
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Qingqing Ou
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
| | - Jia Huang
- Interdisciplinary Materials Research Center, School of Materials Science and Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, P. R. China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University, Shanghai, 200434, P. R. China
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16
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The Relationship between Sparseness and Energy Consumption of Neural Networks. Neural Plast 2020; 2020:8848901. [PMID: 33299397 PMCID: PMC7710421 DOI: 10.1155/2020/8848901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 09/29/2020] [Indexed: 11/17/2022] Open
Abstract
About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes much energy if there are many active neurons in the network. If there are few active neurons in a neural network, the network consumes very little energy. The ratio of active neurons to all neurons of a neural network, that is, the sparseness, affects the energy consumption of a neural network. Laughlin's studies show that the sparseness of an energy-efficient code depends on the balance between signaling and fixed costs. Laughlin did not give an exact ratio of signaling to fixed costs, nor did they give the ratio of active neurons to all neurons in most energy-efficient neural networks. In this paper, we calculated the ratio of signaling costs to fixed costs by the data from physiology experiments. The ratio of signaling costs to fixed costs is between 1.3 and 2.1. We calculated the ratio of active neurons to all neurons in most energy-efficient neural networks. The ratio of active neurons to all neurons in neural networks is between 0.3 and 0.4. Our results are consistent with the data from many relevant physiological experiments, indicating that the model used in this paper may meet neural coding under real conditions. The calculation results of this paper may be helpful to the study of neural coding.
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17
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Elbaz M, Buterman R, Ezra Tsur E. NeuroConstruct-based implementation of structured-light stimulated retinal circuitry. BMC Neurosci 2020; 21:28. [PMID: 32580768 PMCID: PMC7315481 DOI: 10.1186/s12868-020-00578-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 06/17/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Retinal circuitry provides a fundamental window to neural networks, featuring widely investigated visual phenomena ranging from direction selectivity to fast detection of approaching motion. As the divide between experimental and theoretical visual neuroscience is fading, neuronal modeling has proven to be important for retinal research. In neuronal modeling a delicate balance is maintained between bio-plausibility and model tractability, giving rise to myriad modeling frameworks. One biologically detailed framework for neuro modeling is NeuroConstruct, which facilitates the creation, visualization and analysis of neural networks in 3D. RESULTS Here, we extended NeuroConstruct to support the generation of structured visual stimuli, to feature different synaptic dynamics, to allow for heterogeneous synapse distribution and to enable rule-based synaptic connectivity between cell populations. We utilized this framework to demonstrate a simulation of a dense plexus of biologically realistic and morphologically detailed starburst amacrine cells. The amacrine cells were connected to a ganglion cell and stimulated with expanding and collapsing rings of light. CONCLUSIONS This framework provides a powerful toolset for the investigation of the yet elusive underlying mechanisms of retinal computations such as direction selectivity. Particularly, we showcased the way NeuroConstruct can be extended to support advanced field-specific neuro-modeling.
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Affiliation(s)
- Miriam Elbaz
- Jerusalem College of Technology, Jerusalem, Israel
| | | | - Elishai Ezra Tsur
- Jerusalem College of Technology, Jerusalem, Israel. .,Neuro-Biomorphic Engineering Lab, Department of Mathematics and Computer Science, Open University of Israel, Raanana, Israel.
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18
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Yin L, Huang W, Xiao R, Peng W, Zhu Y, Zhang Y, Pi X, Yang D. Optically Stimulated Synaptic Devices Based on the Hybrid Structure of Silicon Nanomembrane and Perovskite. NANO LETTERS 2020; 20:3378-3387. [PMID: 32212734 DOI: 10.1021/acs.nanolett.0c00298] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Optoelectronic synaptic devices have been attracting increasing attention due to their critical role in the development of neuromorphic computing based on optoelectronic integration. Here we start with silicon nanomembrane (Si NM) to fabricate optoelectronic synaptic devices. Organolead halide perovskite (MAPbI3) is exploited to form a hybrid structure with Si NM. We demonstrate that synaptic transistors based on the hybrid structure are very sensitive to optical stimulation with low energy consumption. Synaptic functionalities such as excitatory post-synaptic current (EPSC), paired-pulse facilitation, and transition from short-term memory to long-term memory (LTM) are all successfully mimicked by using these optically stimulated synaptic transistors. The backgate-enabled tunability of the EPSC of these devices further leads to the LTM-based mimicking of visual learning and memory processes under different mood states. This work contributes to the development of Si-based optoelectronic synaptic devices for neuromorphic computing.
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Affiliation(s)
- Lei Yin
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Wen Huang
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Rulei Xiao
- National Laboratory of Solid State Microstructures, Collaborative Innovation Center of Advanced Microstructures and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, China
| | - Wenbing Peng
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Yiyue Zhu
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Yiqiang Zhang
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
- School of Materials Science and Engineering, Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Xiaodong Pi
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - Deren Yang
- State Key Laboratory of Silicon Materials and School of Materials Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China
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19
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Wang R, Fan Y, Wu Y. Spontaneous electromagnetic induction promotes the formation of economical neuronal network structure via self-organization process. Sci Rep 2019; 9:9698. [PMID: 31273270 PMCID: PMC6609776 DOI: 10.1038/s41598-019-46104-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 06/24/2019] [Indexed: 12/16/2022] Open
Abstract
Developed through evolution, brain neural system self-organizes into an economical and dynamic network structure with the modulation of repetitive neuronal firing activities through synaptic plasticity. These highly variable electric activities inevitably produce a spontaneous magnetic field, which also significantly modulates the dynamic neuronal behaviors in the brain. However, how this spontaneous electromagnetic induction affects the self-organization process and what is its role in the formation of an economical neuronal network still have not been reported. Here, we investigate the effects of spontaneous electromagnetic induction on the self-organization process and the topological properties of the self-organized neuronal network. We first find that spontaneous electromagnetic induction slows down the self-organization process of the neuronal network by decreasing the neuronal excitability. In addition, spontaneous electromagnetic induction can result in a more homogeneous directed-weighted network structure with lower causal relationship and less modularity which supports weaker neuronal synchronization. Furthermore, we show that spontaneous electromagnetic induction can reconfigure synaptic connections to optimize the economical connectivity pattern of self-organized neuronal networks, endowing it with enhanced local and global efficiency from the perspective of graph theory. Our results reveal the critical role of spontaneous electromagnetic induction in the formation of an economical self-organized neuronal network and are also helpful for understanding the evolution of the brain neural system.
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Affiliation(s)
- Rong Wang
- College of Science, Xi'an University of Science and Technology, Xi'an, 710054, China.
| | - Yongchen Fan
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures, School of Aerospace, Xi'an Jiaotong University, Xi'an, 710049, China
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20
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Gao S, Liu G, Yang H, Hu C, Chen Q, Gong G, Xue W, Yi X, Shang J, Li RW. An Oxide Schottky Junction Artificial Optoelectronic Synapse. ACS NANO 2019; 13:2634-2642. [PMID: 30730696 DOI: 10.1021/acsnano.9b00340] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
The rapid development of artificial intelligence techniques and future advanced robot systems sparks emergent demand on the accurate perception and understanding of the external environments via visual sensing systems that can co-locate the self-adaptive detecting, processing, and memorizing of optical signals. In this contribution, a simple indium-tin oxide/Nb-doped SrTiO3 (ITO/Nb:SrTiO3) heterojunction artificial optoelectronic synapse is proposed and demonstrated. Through the light and electric field co-modulation of the Schottky barrier profile at the ITO/Nb:SrTiO3 interface, the oxide heterojunction device can respond to the entire visible light region in a neuromorphic manner, allowing synaptic paired-pulse facilitation, short/long-term memory, and "learning-experience" behavior for optical information manipulation. More importantly, the photoplasticity of the artificial synapse has been modulated by heterosynaptic means with a sub-1 V external voltage, not only enabling an optoelectronic analog of the mechanical aperture device showing adaptive and stable optical perception capability under different illuminating conditions but also making the artificial synapse suitable for the mimicry of interest-modulated human visual memories.
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Affiliation(s)
- Shuang Gao
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Gang Liu
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Huali Yang
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Chao Hu
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Qilai Chen
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Guodong Gong
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Wuhong Xue
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Xiaohui Yi
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Jie Shang
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
| | - Run-Wei Li
- CAS Key Laboratory of Magnetic Materials and Devices , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
- Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology , Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences , Ningbo , Zhejiang 315201 , China
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21
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Energy expenditure computation of a single bursting neuron. Cogn Neurodyn 2018; 13:75-87. [PMID: 30728872 PMCID: PMC6339863 DOI: 10.1007/s11571-018-9503-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Revised: 08/22/2018] [Accepted: 08/28/2018] [Indexed: 01/06/2023] Open
Abstract
Brief bursts of high-frequency spikes are a common firing pattern of neurons. The cellular mechanisms of bursting and its biological significance remain a matter of debate. Focusing on the energy aspect, this paper proposes a neural energy calculation method based on the Chay model of bursting. The flow of ions across the membrane of the bursting neuron with or without current stimulation and its power which contributes to the change of the transmembrane electrical potential energy are analyzed here in detail. We find that during the depolarization of spikes in bursting this power becomes negative, which was also discovered in previous research with another energy model. We also find that the neuron’s energy consumption during bursting is minimal. Especially in the spontaneous state without stimulation, the total energy consumption (2.152 × 10−7 J) during 30 s of bursting is very similar to the biological energy consumption (2.468 × 10−7 J) during the generation of a single action potential, as shown in Wang et al. (Neural Plast 2017, 2017a). Our results suggest that this property of low energy consumption could simply be the consequence of the biophysics of generating bursts, which is consistent with the principle of energy minimization. Our results also imply that neural energy plays a critical role in neural coding, which opens a new avenue for research of a central challenge facing neuroscience today.
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